Dynamic Task UI Generation Using LLM and Element Manifests
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Solution Overview
Problem
Traditional hierarchical user interfaces require users to navigate through multiple screens or components, leading to inefficiencies and poor discoverability, especially when completing complex tasks, and existing solutions like wizards are rigid and time-consuming to maintain.
Innovation Solution
A task-oriented user interface is created using task element manifests that contain metadata and properties, allowing a Large Language Model (LLM) to generate dynamic user interfaces by understanding available components and their functionalities, enabling efficient task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a traditional hierarchical user interface is used, then elements are easily discoverable within the hierarchy, but users must navigate through multiple screens or components to complete tasks, increasing navigation complexity and time consumption
Solution Approach 1:
The patent segments the user interface into atomic task elements, each wrapped with a manifest containing metadata about the element's purpose, inputs, outputs, and navigation relationships. This segmentation allows the system to independently identify and group relevant elements for specific tasks without requiring users to navigate through the entire hierarchical structure, thus reducing task completion time while maintaining element discoverability through the task-oriented grouping.
Solution Approach 2:
The patent introduces dynamic UI generation where the interface adapts based on the user's task intent. The system uses Large Language Models to dynamically assemble task elements into a customized interface structure for each task, rather than forcing users through a static hierarchical menu. This dynamic approach reduces navigation time by presenting only relevant elements while maintaining discoverability through structured metadata.
2Loss of time
If task-oriented user interface groupings are created to reduce navigation steps, then task completion becomes more efficient, but discoverability of individual UI elements decreases and manual curation of task flows is required
Solution Approach 1:
The patent implements self-service through automated task flow generation. Instead of requiring manual curation of task flows, the system uses Large Language Models to automatically interpret user task intents and assemble appropriate task elements based on their metadata. The manifests contain structured information about element relationships, inputs, and outputs, enabling the system to autonomously create optimized task sequences without human intervention, thus maintaining discoverability while reducing navigation time.
Solution Approach 2:
The patent changes the organizational parameter of the UI from fixed hierarchical structure to dynamic task-based structure. By transforming the interface organization parameter from 'hierarchy-driven' to 'intent-driven', the system can adapt the grouping of elements based on user needs rather than fixed categories, improving task efficiency while maintaining element discoverability through consistent metadata structure.
3Productivity
If wizards are used to combine hierarchical and task-oriented UIs, then specific tasks can be guided through steps, but the system suffers from poor discoverability, usability and maintainability issues
Solution Approach 1:
The patent creates a universal framework where task elements can serve multiple functions. Each task element is wrapped in a manifest that describes its capabilities, inputs, outputs, and navigation relationships, allowing the same element to be used in different task contexts. This multi-functionality eliminates the need for separate wizard flows for different tasks, improving usability by providing a consistent interface pattern while maintaining task guidance capabilities. The system can adapt to various tasks using the same element library, enhancing maintainability.
Solution Approach 2:
The patent transforms the wizard approach by changing from rigid, task-specific wizard flows to a flexible parameter-driven system. Instead of hardcoding wizard steps for specific tasks, the system uses parameterized task element manifests that can be dynamically combined to create wizard-like guidance for any task. This parameterization approach maintains task guidance functionality while dramatically improving maintainability, as updates to element manifests automatically propagate to all potential task combinations without requiring separate wizard maintenance.
4Productivity
If manual curation of task flows is performed ahead of time, then task-oriented groupings can be created, but the system becomes rigid and time-consuming to maintain
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate and maintain task flows through Large Language Models that process user task intents and assemble appropriate task elements from predefined manifests. This eliminates the need for manual curation and maintenance of task flows, as the system autonomously adapts to new tasks and user needs by interpreting intent and selecting relevant elements, thus reducing maintenance effort while maintaining organized task flows.
Solution Approach 2:
The patent introduces dynamic task flow generation that adapts to changing requirements without manual intervention. The system uses dynamic intent recognition and automated element assembly to create task flows on-demand, rather than relying on static pre-curated flows. This dynamic approach allows the system to respond to new tasks and user needs automatically, maintaining organized task flows while eliminating the time-consuming manual maintenance process.
Data Source
AI summary
A computer-implemented method including associating, at a resource having a plurality of task elements, a task element manifest with each task element from the plurality of task elements, thereby creating a resource map, receiving a request to complete a task; providing the request to complete the task and at least a portion of the resource map to a Large Language Model (LLM); and obtaining, from the LLM, a dynamic user interface to complete the task.